16 research outputs found
A multichannel wireless sEMG sensor endowing a 0.13 μm CMOS mixed-signal SoC
This paper presents a wireless multichannel surface electromyography (sEMG) sensor which features a custom 0.13μm CMOS mixed-signal system-on-chip (SoC) analog frontend circuit. The proposed sensor includes 10 sEMG recording channels with tunable bandwidth (BW) and analog-to-digital converter (ADC) resolution. The SoC includes 10x bioamplifiers, 10x 3 rd order ΔΣ MASH 1-1-1 ADC, and 10x on-chip decimation filters (DF). This SoC provides the sEMG samples data through a serial peripheral interface (SPI) bus to a microcontroller unit (MCU) that then transfers the data to a wireless transceiver. We report sEMG waveforms acquired using a custom multichannel electrode module, and a comparison with a commercial grade system. Results show that the proposed integrated wireless SoC-based system compares well with the commercial grade sEMG recording system. The sensor has an input-referred noise of 2.5 μVrms (BW of 10-500 Hz), an input-dynamic range of 6 mVpp, a programmable sampling rate of 2 ksps, for sEMG, while consuming only 7.1 μW/Ch for the SoC (w/ ADC & DF) and 21.8 mW of power for the sensor (Transceiver, MCU, etc.). The system lies on a 1.5 × 2.0 cm 2 printed circuit board and weights <; 1 g
Intersected EMG heatmaps and deep learning based gesture recognition
Hand gesture recognition in myoelectric based prosthetic devices is a key challenge to offering effective solutions to hand/lower arm amputees. A novel hand gesture recognition methodology that employs the difference of EMG energy heatmaps as the input of a specific designed deep learning neural network is presented. Experimental results using data from real amputees indicate that the proposed design achieves 94.31% as average accuracy with best accuracy rate of 98.96%. A comparison of experimental results between the proposed novel hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design
Deep learning approach to control of prosthetic hands with electromyography signals
Natural muscles provide mobility in response to nerve impulses.
Electromyography (EMG) measures the electrical activity of muscles in response
to a nerve's stimulation. In the past few decades, EMG signals have been used
extensively in the identification of user intention to potentially control
assistive devices such as smart wheelchairs, exoskeletons, and prosthetic
devices. In the design of conventional assistive devices, developers optimize
multiple subsystems independently. Feature extraction and feature description
are essential subsystems of this approach. Therefore, researchers proposed
various hand-crafted features to interpret EMG signals. However, the
performance of conventional assistive devices is still unsatisfactory. In this
paper, we propose a deep learning approach to control prosthetic hands with raw
EMG signals. We use a novel deep convolutional neural network to eschew the
feature-engineering step. Removing the feature extraction and feature
description is an important step toward the paradigm of end-to-end
optimization. Fine-tuning and personalization are additional advantages of our
approach. The proposed approach is implemented in Python with TensorFlow deep
learning library, and it runs in real-time in general-purpose graphics
processing units of NVIDIA Jetson TX2 developer kit. Our results demonstrate
the ability of our system to predict fingers position from raw EMG signals. We
anticipate our EMG-based control system to be a starting point to design more
sophisticated prosthetic hands. For example, a pressure measurement unit can be
added to transfer the perception of the environment to the user. Furthermore,
our system can be modified for other prosthetic devices.Comment: Conference. Houston, Texas, USA. September, 201
Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition
Conventional electromyography (EMG) measures the continuous neural activity
during muscle contraction, but lacks explicit quantification of the actual
contraction. Mechanomyography (MMG) and accelerometers only measure body
surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic
snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle
actuation sensing that can be wearable and touchless, capturing both
superficial and deep muscle groups. We verified RMG experimentally by a forearm
wearable sensor for detailed hand gesture recognition. We first converted the
radio sensing outputs to the time-frequency spectrogram, and then employed the
vision transformer (ViT) deep learning network as the classification model,
which can recognize 23 gestures with an average accuracy up to 99% on 8
subjects. By transfer learning, high adaptivity to user difference and sensor
variation were achieved at an average accuracy up to 97%. We further
demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for
eye movement and body postures tracking. RMG can be used with synchronous EMG
to derive stimulation-actuation waveforms for many future applications in
kinesiology, physiotherapy, rehabilitation, and human-machine interface
From Unimodal to Multimodal: improving the sEMG-Based Pattern Recognition via deep generative models
Multimodal hand gesture recognition (HGR) systems can achieve higher
recognition accuracy. However, acquiring multimodal gesture recognition data
typically requires users to wear additional sensors, thereby increasing
hardware costs. This paper proposes a novel generative approach to improve
Surface Electromyography (sEMG)-based HGR accuracy via virtual Inertial
Measurement Unit (IMU) signals. Specifically, we trained a deep generative
model based on the intrinsic correlation between forearm sEMG signals and
forearm IMU signals to generate virtual forearm IMU signals from the input
forearm sEMG signals at first. Subsequently, the sEMG signals and virtual IMU
signals were fed into a multimodal Convolutional Neural Network (CNN) model for
gesture recognition. To evaluate the performance of the proposed approach, we
conducted experiments on 6 databases, including 5 publicly available databases
and our collected database comprising 28 subjects performing 38 gestures,
containing both sEMG and IMU data. The results show that our proposed approach
outperforms the sEMG-based unimodal HGR method (with increases of
2.15%-13.10%). It demonstrates that incorporating virtual IMU signals,
generated by deep generative models, can significantly enhance the accuracy of
sEMG-based HGR. The proposed approach represents a successful attempt to
transition from unimodal HGR to multimodal HGR without additional sensor
hardware
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
Predicting lower limb motion intent is vital for controlling exoskeleton
robots and prosthetic limbs. Surface electromyography (sEMG) attracts
increasing attention in recent years as it enables ahead-of-time prediction of
motion intentions before actual movement. However, the estimation performance
of human joint trajectory remains a challenging problem due to the inter- and
intra-subject variations. The former is related to physiological differences
(such as height and weight) and preferred walking patterns of individuals,
while the latter is mainly caused by irregular and gait-irrelevant muscle
activity. This paper proposes a model integrating two gait cycle-inspired
learning strategies to mitigate the challenge for predicting human knee joint
trajectory. The first strategy is to decouple knee joint angles into motion
patterns and amplitudes former exhibit low variability while latter show high
variability among individuals. By learning through separate network entities,
the model manages to capture both the common and personalized gait features. In
the second, muscle principal activation masks are extracted from gait cycles in
a prolonged walk. These masks are used to filter out components unrelated to
walking from raw sEMG and provide auxiliary guidance to capture more
gait-related features. Experimental results indicate that our model could
predict knee angles with the average root mean square error (RMSE) of
3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best
performance in relevant literatures that has been reported, with reduced RMSE
by at least 9.5%
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
The research in myoelectric control systems primarily focuses on extracting
discriminative representations from the electromyographic (EMG) signal by
designing handcrafted features. Recently, deep learning techniques have been
applied to the challenging task of EMG-based gesture recognition. The adoption
of these techniques slowly shifts the focus from feature engineering to feature
learning. However, the black-box nature of deep learning makes it hard to
understand the type of information learned by the network and how it relates to
handcrafted features. Additionally, due to the high variability in EMG
recordings between participants, deep features tend to generalize poorly across
subjects using standard training methods. Consequently, this work introduces a
new multi-domain learning algorithm, named ADANN, which significantly enhances
(p=0.00004) inter-subject classification accuracy by an average of 19.40%
compared to standard training. Using ADANN-generated features, the main
contribution of this work is to provide the first topological data analysis of
EMG-based gesture recognition for the characterisation of the information
encoded within a deep network, using handcrafted features as landmarks. This
analysis reveals that handcrafted features and the learned features (in the
earlier layers) both try to discriminate between all gestures, but do not
encode the same information to do so. Furthermore, using convolutional network
visualization techniques reveal that learned features tend to ignore the most
activated channel during gesture contraction, which is in stark contrast with
the prevalence of handcrafted features designed to capture amplitude
information. Overall, this work paves the way for hybrid feature sets by
providing a clear guideline of complementary information encoded within learned
and handcrafted features.Comment: The first two authors shared first authorship. The last three authors
shared senior authorship. 32 page
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
In recent years, deep learning algorithms have become increasingly more
prominent for their unparalleled ability to automatically learn discriminant
features from large amounts of data. However, within the field of
electromyography-based gesture recognition, deep learning algorithms are seldom
employed as they require an unreasonable amount of effort from a single person,
to generate tens of thousands of examples.
This work's hypothesis is that general, informative features can be learned
from the large amounts of data generated by aggregating the signals of multiple
users, thus reducing the recording burden while enhancing gesture recognition.
Consequently, this paper proposes applying transfer learning on aggregated data
from multiple users, while leveraging the capacity of deep learning algorithms
to learn discriminant features from large datasets. Two datasets comprised of
19 and 17 able-bodied participants respectively (the first one is employed for
pre-training) were recorded for this work, using the Myo Armband. A third Myo
Armband dataset was taken from the NinaPro database and is comprised of 10
able-bodied participants. Three different deep learning networks employing
three different modalities as input (raw EMG, Spectrograms and Continuous
Wavelet Transform (CWT)) are tested on the second and third dataset. The
proposed transfer learning scheme is shown to systematically and significantly
enhance the performance for all three networks on the two datasets, achieving
an offline accuracy of 98.31% for 7 gestures over 17 participants for the
CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw
EMG-based ConvNet. Finally, a use-case study employing eight able-bodied
participants suggests that real-time feedback allows users to adapt their
muscle activation strategy which reduces the degradation in accuracy normally
experienced over time.Comment: Source code and datasets available:
https://github.com/Giguelingueling/MyoArmbandDatase